Ants can learn from the opposite

Nicolás Rojas-Morales, Riff R. María-Cristina, Elizabeth Montero

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

3 Citas (Scopus)

Resumen

In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.

Idioma originalInglés
Título de la publicación alojadaGECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditoresTobias Friedrich
EditorialAssociation for Computing Machinery, Inc
Páginas389-396
Número de páginas8
ISBN (versión digital)9781450342063
DOI
EstadoPublicada - 20 jul 2016
Evento2016 Genetic and Evolutionary Computation Conference, GECCO 2016 - Denver, Estados Unidos
Duración: 20 jul 201624 jul 2016

Conferencia

Conferencia2016 Genetic and Evolutionary Computation Conference, GECCO 2016
PaísEstados Unidos
CiudadDenver
Período20/07/1624/07/16

Áreas temáticas de ASJC Scopus

  • Informática aplicada
  • Teoría computacional y matemáticas
  • Software

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  • Citar esto

    Rojas-Morales, N., María-Cristina, R. R., & Montero, E. (2016). Ants can learn from the opposite. En T. Friedrich (Ed.), GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 389-396). Association for Computing Machinery, Inc. https://doi.org/10.1145/2908812.2908927